{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
   "metadata": {},
   "source": [
    "# Day 3 - Conversational AI - aka Chatbot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from typing import List\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import google.generativeai\n",
    "# import anthropic\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "231605aa-fccb-447e-89cf-8b187444536a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables in a file called .env\n",
    "\n",
    "load_dotenv()\n",
    "os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "google.generativeai.configure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are a helpful assistant\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba2123e7-77ed-43b4-8c37-03658fb42b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are an assistant that is great at telling jokes\"\n",
    "user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"\n",
    "\n",
    "prompts = [\n",
    "    {\"role\": \"system\", \"content\": system_message},\n",
    "    {\"role\": \"user\", \"content\": user_prompt}\n",
    "  ]\n",
    "\n",
    "# The API for Gemini has a slightly different structure.\n",
    "# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n",
    "# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
    "\n",
    "gemini = google.generativeai.GenerativeModel(\n",
    "    model_name='gemini-1.5-flash',\n",
    "    system_instruction=system_message\n",
    ")\n",
    "response = gemini.generate_content(user_prompt)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b933ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import google.generativeai as genai\n",
    "\n",
    "model = genai.GenerativeModel('gemini-1.5-flash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91578b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat = model.start_chat(history=[])\n",
    "response = chat.send_message('Hello! My name is Shardul.')\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4bc38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = chat.send_message('Can you tell something interesting about star wars?')\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337bee91",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = chat.send_message('Do you remember what my name is?')\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcaf4d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat.history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
   "metadata": {},
   "source": [
    "# Please read this! A change from the video:\n",
    "\n",
    "In the video, I explain how we now need to write a function called:\n",
    "\n",
    "`chat(message, history)`\n",
    "\n",
    "Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\n",
    "\n",
    "```\n",
    "[\n",
    "    {\"role\": \"system\", \"content\": \"system message here\"},\n",
    "    {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
    "    {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
    "    {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
    "]\n",
    "```\n",
    "\n",
    "But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n",
    "\n",
    "So our work just got easier!\n",
    "\n",
    "We will write a function `chat(message, history)` where:  \n",
    "**message** is the prompt to use  \n",
    "**history** is the past conversation, in OpenAI format  \n",
    "\n",
    "We will combine the system message, history and latest message, then call OpenAI ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    relevant_system_message = system_message\n",
    "    if 'belt' in message:\n",
    "        relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
    "    \n",
    "    messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "\n",
    "    stream = gemini.generate_content(message, safety_settings=[\n",
    "        {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"},\n",
    "        {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n",
    "        {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n",
    "        {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"}], stream=True)\n",
    "\n",
    "    response = \"\"\n",
    "    for chunk in stream:\n",
    "        print(chunk)  # Print the chunk to understand its structure\n",
    "        # Adjust the following line based on the actual structure of the chunk\n",
    "        response += chunk.get('content', '') or ''\n",
    "        yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6e745e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_model = genai.GenerativeModel('gemini-1.5-flash')\n",
    "chat = chat_model.start_chat()\n",
    "\n",
    "msg = \"what is gen ai\"\n",
    "stream = chat.send_message(msg, stream=True)\n",
    "# print(\"Response:\", stream.text)\n",
    "for chunk in stream:\n",
    "    print(chunk.text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dce941ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "chat = model.start_chat(history=[])\n",
    "\n",
    "# Transform Gradio history to Gemini format\n",
    "def transform_history(history):\n",
    "    new_history = []\n",
    "    for chat in history:\n",
    "        new_history.append({\"parts\": [{\"text\": chat[0]}], \"role\": \"user\"})\n",
    "        new_history.append({\"parts\": [{\"text\": chat[1]}], \"role\": \"model\"})\n",
    "    return new_history\n",
    "\n",
    "def response(message, history):\n",
    "    global chat\n",
    "    # The history will be the same as in Gradio, the 'Undo' and 'Clear' buttons will work correctly.\n",
    "    chat.history = transform_history(history)\n",
    "    response = chat.send_message(message)\n",
    "    response.resolve()\n",
    "\n",
    "    # Each character of the answer is displayed\n",
    "    for i in range(len(response.text)):\n",
    "        time.sleep(0.01)\n",
    "        yield response.text[: i+1]\n",
    "\n",
    "gr.ChatInterface(response,\n",
    "                 textbox=gr.Textbox(placeholder=\"Question to Gemini\")).launch(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#181;\">Business Applications</h2>\n",
    "            <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
    "<br/><br/>\n",
    "Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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